




'''simple docstring'''


import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
UpperCAmelCase_					=   0B1_0_1_1_0_0_1_1_1_1_1_0_1_1_0_0_1_0_0_1_0_0_0_0_0_1_1_1_1_0_1_1_1_0_1_1_0_0_0_1_1_0_0_1_1_1_1_0
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
UpperCAmelCase_					=   [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]





class  __lowercase    :







   def __init__(						self   )					->     str:
     __a								=       WATERMARK_BITS
     __a								=       WatermarkEncoder()

     self.encoder.set_watermark('bits'   ,				self.watermark   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     List[Any]:
     # can't encode images that are smaller than 256
     if images.shape[-1] < 256:
       return images

     __a								=       (255 * (images / 2 + 0.5)).cpu().permute(0   ,				2   ,				3   ,				1   ).float().numpy()

     __a								=       [self.encoder.encode(UpperCamelCase   ,				'dwtDct'   ) for image in images]

     __a								=       torch.from_numpy(np.array(UpperCamelCase   )   ).permute(0   ,				3   ,				1   ,				2   )

     __a								=       torch.clamp(2 * (images / 255 - 0.5)   ,				min=-1.0   ,				max=1.0   )
     return images









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					=   {
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ClapModel",
      "ClapPreTrainedModel",
      "ClapTextModel",
      "ClapTextModelWithProjection",
      "ClapAudioModel",
      "ClapAudioModelWithProjection",
  ]
  UpperCAmelCase_					=   ["ClapFeatureExtractor"]

if TYPE_CHECKING:
  from .configuration_clap import (
      CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
      ClapAudioConfig,
      ClapConfig,
      ClapTextConfig,
  )
  from .processing_clap import ClapProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_clap import ClapFeatureExtractor
    from .modeling_clap import (
        CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
        ClapAudioModel,
        ClapAudioModelWithProjection,
        ClapModel,
        ClapPreTrainedModel,
        ClapTextModel,
        ClapTextModelWithProjection,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



